Claude
3a977c7438
Lets SAM, U2Net, BEN2, and BiRefNet-HR (optionally SDXL via --sdxl) be downloaded outside Docker into ./data/, which is already bind-mounted into the GPU container — so a blocked container network no longer blocks first-run setup. Reuses the existing dual-mode download_sam_model.py and download_u2net_model.py as-is. For the HuggingFace Hub models, sets HF_HOME (rather than --cache-dir) so the host-side cache layout matches the container's default ~/.cache/huggingface resolution exactly, avoiding a path-nesting mismatch between the two. Wired into install-local-gpu.sh's completion banner and bring-up-local-gpu.sh's header, and referenced from the relevant README troubleshooting sections and the hf_cache bind-mount comment in docker-compose.gpu.yml.
291 lines
11 KiB
Markdown
291 lines
11 KiB
Markdown
# EditmaskwithAI
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A self-hosted, web-based AI photo editor. Paint over any object, describe what you want, and the AI replaces just that region — every pixel outside your selection stays untouched.
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## Quick Start
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### GPU machine (recommended — free inference, best quality)
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```bash
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git clone https://github.com/outis1one/editmaskwithai
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cd editmaskwithai
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# One-time setup: installs nvidia-container-toolkit, configures Docker,
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# and sets up a permanent DNS fix so the container can download models.
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chmod +x install-local-gpu.sh
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./install-local-gpu.sh
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# Start the app (run this each time):
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chmod +x bring-up-local-gpu.sh
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./bring-up-local-gpu.sh
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```
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Open **http://localhost:3080**
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**First startup downloads the AI model for your GPU (~13 GB, one time).** Models are cached in `./data/hf_cache/` and survive rebuilds.
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---
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### Cloud API (no GPU required)
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```bash
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git clone https://github.com/outis1one/editmaskwithai
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cd editmaskwithai
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cp .env.example .env
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# Edit .env: set AI_PROVIDER and your API key (see .env.example for options)
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docker compose up -d --build
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```
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Open **http://localhost:3080**
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---
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### Updates (any machine)
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```bash
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git pull
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# GPU:
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./bring-up-local-gpu.sh
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# or cloud (no GPU):
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docker compose up -d --build
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```
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If pip packages seem stale after a pull (e.g., wrong diffusers version), force a pip layer rebuild without re-downloading the entire PyTorch base image:
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```bash
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BUILDID=$(date +%s) ./bring-up-local-gpu.sh
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```
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---
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## AI Providers
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| Provider | Setup | Cost | Quality |
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|---|---|---|---|
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| `local_gpu` | GPU machine + nvidia-container-toolkit | Free | Best (SDXL/FLUX auto-selected by VRAM) |
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| `openai` | `OPENAI_API_KEY=sk-...` | ~$0.02–0.04/image | DALL-E 3 |
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| `replicate` | `REPLICATE_API_KEY=r8_...` | ~$0.002–0.03/image | Multiple models |
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| `invokeai` | InvokeAI running on another machine | Self-hosted | FLUX/SDXL |
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| `comfyui` | ComfyUI running on another machine | Self-hosted | Any model |
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You can also mix: set a default provider in `.env` and override per-operation in the **Image → AI Provider Settings** dialog inside the app.
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---
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## GPU Tier Auto-Selection
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The app detects your GPU at startup and picks the best model it can run:
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| Effective VRAM | Model selected | Notes |
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|---|---|---|
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| ≥ 24 GB | FLUX.1-schnell | Best quality, 4-step generation |
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| 12–24 GB | SDXL | Excellent quality |
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| 8–12 GB | SDXL + xformers | Good quality |
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| 6–8 GB | SDXL + attention slicing | Good quality, slightly slower |
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| 4–6 GB | SDXL + CPU offload | Good quality, slower (GTX 1060 6GB range) |
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| 2–4 GB | SD 1.5 | Fast, lower detail |
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| < 2 GB | SD 1.5 + CPU offload | Very slow — consider a cloud provider |
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Override the auto-selected model with `HF_MODEL_TXT2IMG`, `HF_MODEL_INPAINT` in `.env`.
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---
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## What it can do
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### Selection
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- **Smart Select (SAM brush)** — paint over an object, AI detects its exact boundaries
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- **Smart Select (click)** — click any object, SAM selects it
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- **Rectangle / Ellipse / Lasso** — classic selection tools
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### After selecting
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- **AI Edit** — describe what to change ("add a scar", "make it look aged")
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- **Make less symmetrical** — AI adds natural organic variation
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- **Replace with clipboard** — paste any image into the selection shape
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- **Scale by %** — make the selected object bigger/smaller, AI fills the gap
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- **Copy / Cut to layer** — non-destructive layer workflow
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- **Erase** — remove the selected region with AI fill
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### Image tools
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- **Text → Image** — generate from a text description (GPU or cloud)
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- **Upscale** — Real-ESRGAN AI upscaling (genuinely adds detail, not just resize)
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- **Prepare for Print** — one-click: AI upscale to target DPI + fit to frame
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- **Fit to Frame** — resize/crop/AI-extend to standard print sizes
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- **Expand Canvas (Outpaint)** — AI extends the image in any direction
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- **Remove Background** — one-click background removal (BEN2 by default, BiRefNet-HR or U2Net selectable)
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### Print presets
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Frame sizes: 4×6, 5×7, 8×10, 11×14, 16×20, 18×24, 20×24, 24×36 (portrait + landscape)
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DPI options: 72, 150, 200, 300 — 200 DPI is fine for 18×24" and larger (viewed from distance)
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---
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## Progress bars
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All AI operations show a real-time progress overlay. For local GPU inference, the bar advances step-by-step as the model denoises (e.g. "Step 14 / 30"). For cloud providers and upscale operations, it animates to indicate activity.
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---
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## Logs
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```bash
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# GPU container:
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docker compose -f docker-compose.gpu.yml logs -f
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# Standard container:
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docker compose logs -f
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```
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---
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## File structure
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```
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EditmaskwithAI/
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├── backend/
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│ ├── app/
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│ │ ├── routers/ # API endpoints (ai_tools, print_tools, …)
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│ │ ├── services/ # gpu_detect, local_diffusion, upscale, …
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│ │ └── config.py
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│ ├── requirements.txt
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│ └── requirements.gpu.txt
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├── frontend/
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│ └── src/js/
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│ ├── tools/ # brush_select (SAM paint), smart_select, …
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│ ├── modules/
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│ │ ├── generate/ # text_to_image, outpaint
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│ │ └── image/ # upscale, frame_fit, print_prepare, …
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│ └── libs/
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│ └── progress_overlay.js
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├── docker-compose.yml # Cloud / no-GPU
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├── docker-compose.gpu.yml # NVIDIA GPU (recommended)
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├── docker-compose.dev.yml # Dev with hot reload
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├── Dockerfile
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├── Dockerfile.gpu
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├── install-local-gpu.sh # One-time GPU host setup
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├── bring-up-local-gpu.sh # Start/stop the GPU container
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├── prefetch-models.sh # Download AI models on the host (DNS-blocked workaround)
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└── .env.example
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```
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---
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## Troubleshooting
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**GPU not detected in Docker**
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```bash
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# Check toolkit is installed and Docker restarted:
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docker run --rm --gpus all nvidia/cuda:12.1.0-base-ubuntu22.04 nvidia-smi
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# If that fails, re-run: sudo nvidia-ctk runtime configure --runtime=docker && sudo systemctl restart docker
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```
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**Model download stalls or fails**
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```bash
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# Check logs for HuggingFace errors:
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docker compose -f docker-compose.gpu.yml logs -f | grep -E "local_gpu|Error|Failed"
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# If a private/gated model: add HF_TOKEN=hf_... to .env
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```
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**SAM model fails to download (DNS error / firewall blocking port 53)**
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If the container can't reach `dl.fbaipublicfiles.com` (you'll see `Errno -3 Name or service not known` in the logs), download SAM directly on the host and let the bind mount make it visible to the container — no rebuild needed:
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```bash
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./prefetch-models.sh
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# or manually:
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mkdir -p ./data/models
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# sudo needed if ./data/ was created by Docker (root-owned):
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sudo curl -L -o ./data/models/sam_vit_b_01ec64.pth \
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https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth
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```
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The file is ~375 MB. Once it exists at `./data/models/sam_vit_b_01ec64.pth`, the container picks it up on the next startup (no rebuild required). Verify with:
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```bash
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docker compose -f docker-compose.gpu.yml logs | grep -i sam
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# Should show: "SAM model loaded on cuda" (or cpu)
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```
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If Docker created `./data/` as root and you can't write there without `sudo`, you can also use root's curl as above — the container reads the file regardless of owner.
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**Remove Background fails ("Install ben2, u2net, or rembg")**
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Remove Background tries, in order: the model set by `BG_REMOVAL_MODEL` (default `ben2`), then the other local models, then `rembg` as a last resort. You'll see this error only if all of them fail.
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- **ben2 / birefnet-hr** (GPU image only) download their weights from HuggingFace on first use, cached under `./data/hf_cache`. If that download fails (DNS/firewall, see above), run `./prefetch-models.sh` to fetch both directly on the host, or check the logs for the specific error:
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```bash
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docker compose -f docker-compose.gpu.yml logs -f | grep -iE "ben2|birefnet"
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```
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- **u2net** auto-downloads (~176MB) from GitHub on first use, same as SAM. If that fails too, download it directly on the host:
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```bash
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./prefetch-models.sh
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# or manually:
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mkdir -p ./data/models
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sudo curl -L -o ./data/models/u2net.onnx \
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https://github.com/danielgatis/rembg/releases/download/v0.0.0/u2net.onnx
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```
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The file is ~176 MB. Once it exists at `./data/models/u2net.onnx`, the next "Remove Background" click picks it up — no rebuild or restart needed. Verify with:
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```bash
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docker compose logs -f | grep -i u2net
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# Should show: "U2Net model loaded successfully with OpenCV DNN"
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```
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You can also pick a specific model per-edit from the Remove Background dialog's model dropdown, overriding `BG_REMOVAL_MODEL` for that one call.
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**AI models not downloading (container DNS blocked)**
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Easiest fix: download the models on the host instead of inside the container — they land in `./data/`, which is already bind-mounted into the container, so it picks them up with no rebuild:
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```bash
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./prefetch-models.sh # SAM + U2Net + BEN2 + BiRefNet-HR (~1.5GB)
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./prefetch-models.sh --sdxl # also Text→Image / AI Edit models (~13GB)
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./bring-up-local-gpu.sh
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```
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If that also fails to reach the network, the problem is host-level (firewall/DNS), not Docker-specific — see your network/firewall configuration.
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Alternatively, if you ran `./install-local-gpu.sh`, container DNS is already permanently fixed via a systemd-managed iptables rule. If you skipped that script, apply the same fix manually (does **not** affect container isolation):
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```bash
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sudo iptables -I DOCKER-USER -p udp --dport 53 -j ACCEPT
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./bring-up-local-gpu.sh
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```
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The container will now resolve hostnames and download models automatically (~13 GB on first run, then cached). Watch progress:
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```bash
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docker compose -f docker-compose.gpu.yml logs -f | grep -E "local_gpu|Cached|failed"
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```
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**Alternative: download with a Docker helper container** (no host Python needed):
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```bash
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# Inpainting model (~6.5 GB) — needed for AI Edit, Make less symmetrical, etc.
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docker run --rm \
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-v "$(pwd)/data/hf_cache:/root/.cache/huggingface" \
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python:3.11-slim \
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bash -c "pip install -q huggingface-hub && \
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huggingface-cli download diffusers/stable-diffusion-xl-1.0-inpainting-0.1 \
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--exclude '*.msgpack' 'flax_*' 'tf_*'"
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# Text-to-image model (~6.5 GB) — needed for Text → Image
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docker run --rm \
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-v "$(pwd)/data/hf_cache:/root/.cache/huggingface" \
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python:3.11-slim \
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bash -c "pip install -q huggingface-hub && \
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huggingface-cli download stabilityai/stable-diffusion-xl-base-1.0 \
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--exclude '*.msgpack' 'flax_*' 'tf_*'"
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```
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Then restart: `docker compose -f docker-compose.gpu.yml restart`
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**Out of VRAM during generation**
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- Reduce `LOCAL_GPU_MAX_PIPELINES=1` in `.env` (default 2)
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- Or override to a smaller model: `HF_MODEL_TXT2IMG=runwayml/stable-diffusion-v1-5`
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**Settings saved locally only**
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- The in-app AI Provider Settings dialog saves to localStorage for the session
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- To make settings permanent: edit `.env` and rebuild
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**Check API docs**
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```
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http://localhost:3080/api/docs
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```
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